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Presumably the presence of noise destroys entanglement, but since the measurement itself is noisy, it is not certain how much is destroyed and how much is simply a bad measurement. 9 1 FIG. 0069 noise (orange; light gray bottom curve in print version). 013 noise and then tested at the given level. is obvious from the results that the QNN technique does an excellent job of remaining robust to pure noise. Second, we consider testing on mixed states. We might expect the QNN to perform significantly less well with these kinds of states because the training set (Table 1) contained no mixed states.
Section 4 shows that QC can be useful during the training phase, while Section 5 reviews the techniques for MS and EE and shows how these techniques can exploit the advantages of the QC paradigm. Finally, in Section 6 the conclusions and the future perspectives regarding the issues addressed in the chapter are presented. 2 THE SUPERVISED LEARNING PROBLEM: TRAINING, MODEL SELECTION, AND ERROR ESTIMATION As first step let us recall the conventional SL framework, in particular the binary classification problem, and some common definitions [1,65,67].
W. Cross, G. A. Smolin, Quantum learning robust against noise, Phys. Rev. A 92 (2015) 012327. -L. Hu, H. Fan, Robustness of quantum correlations against decoherence, Ann. Phys. 327 (2012) 851–860. S. R. H. 4963v3. C. Behrman, J. E. R. Skinner, A quantum dot neural network, in: Proceedings of the Fourth Workshop on Physics and Computation (PhysComp96), vol. 22, 1996. C. R. E. R. Skinner, A quantum Hopfield network, in: Proceedings of the Fifth Joint Conference on Information Sciences, vol. 1, 2000, p.
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